SoroushVahidi/scidocs huggingface dataset:
Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data
(2014)

Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.
FOR LEAP MOTION CONTROLLER
(2014)

This thesis studies the new possibilities to gesture interfaces that emerged with a Leap Motion sensor. The Leap Motion is an innovative, 3D motion capturing device designed especially for hands and fingers tracking with precision up to 0.01mm. The outcome of the thesis is the LeapGesture library dedicated to the developers for Leap Motion Controller that contains algorithms allowing to learn and recognize gestures. The authors examined the data provided by the sensor in context of recognition of hand poses (static gestures), hand movements (dynamic gestures) and in task of a finger recognition. The static gestures are recognized using the Support Vector Machine (SVM) with median filtering an input data and using the correspondences between consecutive recognitions. The thesis contains evaluation of different feature sets, which have a significant impact on the recognition rate. The chosen feature set allowed to recognize a set of five gestures with 99% accuracy and a set of ten gestures with 85%. The dynamic gestures (movements of a hand and fingers) are recognized with the Hidden Markov Models (HMM). Recognition with HMMs allowed to achieve accuracy of 80% for a set containing six classes of dynamic gestures. Finger recognition algorithms proposed in this thesis works with 93% accuracy on a recorded dataset. The LeapGesture library contains presented approaches using a C++ interface, that can be easily used in any application for many purposes.
A Hybrid Ant Colony Optimization Algorithm for Path Planning of Robot in Dynamic Environment
(2006)

Ant colony optimization and artificial potential field were used respectively as global path planning and local path planning methods in this paper. Some modifications were made to accommodate ant colony optimization to path planning. Pheromone generated by ant colony optimization was also utilized to prevent artificial potential field from getting local minimum. Simulation results showed that the hybrid algorithm could satisfy the real-time demand. The comparison between ant colony optimization and genetic algorithm was also made in this paper.
Co-designing accelerators and SoC interfaces using gem5-Aladdin
(2016)

Increasing demand for power-efficient, high-performance computing has spurred a growing number and diversity of hardware accelerators in mobile and server Systems on Chip (SoCs). This paper makes the case that the co-design of the accelerator microarchitecture with the system in which it belongs is critical to balanced, efficient accelerator microarchitectures. We find that data movement and coherence management for accelerators are significant yet often unaccounted components of total accelerator runtime, resulting in misleading performance predictions and inefficient accelerator designs. To explore the design space of accelerator-system co-design, we develop gem5-Aladdin, an SoC simulator that captures dynamic interactions between accelerators and the SoC platform, and validate it to within 6% against real hardware. Our co-design studies show that the optimal energy-delay-product (EDP) of an accelerator microarchitecture can improve by up to 7.4× when system-level effects are considered compared to optimizing accelerators in isolation.
Compatibility Family Learning for Item Recommendation and Generation
(2018)

Compatibility between items, such as clothes and shoes, is a major factor among customer’s purchasing decisions. However, learning “compatibility” is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed. We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space. These prototypes reflect the broad notions of compatibility. We refer to both the embedding and prototypes as “Compatibility Family”. In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item. We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset. Our method consistently achieves state-of-the-art performance. Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item. We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items. Our generated images are significantly preferred, with roughly twice the number of votes as others.
ON SUPERVISED AND SEMI-SUPERVISED k-NEAREST NEIGHBOR ALGORITHMS
(2008)

The k-nearest neighbor (kNN) is one of the simplest classification methods used in machine learning. Since the main component of kNN is a distance metric, kernelization of kNN is possible. In this paper kNN and semi-supervised kNN algorithms are empirically compared on two data sets (the USPS data set and a subset of the Reuters-21578 text categorization corpus). We use a soft version of the kNN algorithm to handle multi-label classification settings. Semi-supervision is performed by using data-dependent kernels.
GaN HEMT based Doherty amplifier for 3.5-GHz WiMAX Applications
(2007)

We have implemented a Doherty amplifier for 3.5-GHz World Interoperability for Microwave Access (WiMAX) applications using Eudyna 90-W (P3 dB) Gallium-Nitride (GaN) High Electron Mobility Transistor (HEMT) because of the poor efficiency of a standard class AB amplifier when the linearity performance is good. The load modulation performance of the GaN HEMT device for the Doherty operation is rather moderate but workable. The linearity is improved using the in-band error cancellation technique of the Doherty amplifier. The implemented Doherty amplifier has been designed at an average output power of 43 dBm, backed-off about 8 dB from the 51 dBm (P3 dB). For WiMAX signal with 28 MHz signal bandwidth, the measured drain efficiency of the amplifier is 27.8%, and the measured Relative Constellation Error (RCE) is -33.17 dB, while those of the comparable class AB amplifier are 19.42% and -24.26 dB, respectively, at the same average output power level.
MICS: an efficient content space representation model for publish/subscribe systems
(2009)

One of the main challenges faced by content-based publish/subscribe systems is handling large amount of dynamic subscriptions and publications in a multidimensional content space. To reduce subscription forwarding load and speed up content matching, subscription covering, subsumption and merging techniques have been proposed. In this paper we propose MICS, Multidimensional Indexing for Content Space that provides an efficient representation and processing model for large number of subscriptions and publications. MICS creates a one dimensional representation for publications and subscriptions using Hilbert space filling curve. Based on this representation, we propose novel content matching and subscription management (covering, subsumption and merging) algorithms. Our experimental evaluation indicates that the proposed approach significantly speeds up subscription management operations compared to the naive linear approach.
Named Entity Recognition and Compositional Morphology for Word Representations in Chinese
(2017)

We approach the task of named entity recognition (NER) for Chinese by representing each entity with a composition of its traits: the token-entity, its characters, and its characters’ main radicals. Character and radical-level information for each entity are included to provide additional relationships that might not be strictly captured within a token-entity’s word embedding during training. We learn using neural networks that are some combination of the following traits: unidirectional or bidirectional; single or multi-layer; simple, gated recurrent unit (GRU), or long short term memory (LSTM) celled. We achieve a maximum not-O token-level F1 score of 76 and entity-level F1 of 70.
The extraction of neural strategies from the surface EMG.
(2004)

This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.
Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions
(2014)

In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users' devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.
Device-to-device communication as an underlay to LTE-advanced networks
(2009)

In this article device-to-device (D2D) communication underlaying a 3GPP LTE-Advanced cellular network is studied as an enabler of local services with limited interference impact on the primary cellular network. The approach of the study is a tight integration of D2D communication into an LTE-Advanced network. In particular, we propose mechanisms for D2D communication session setup and management involving procedures in the LTE System Architecture Evolution. Moreover, we present numerical results based on system simulations in an interference limited local area scenario. Our results show that D2D communication can increase the total throughput observed in the cell area.
Design aspects of network assisted device-to-device communications
(2012)

Device-to-device (D2D) communications underlaying a cellular infrastructure has been proposed as a means of taking advantage of the physical proximity of communicating devices, increasing resource utilization, and improving cellular coverage. Relative to the traditional cellular methods, there is a need to design new peer discovery methods, physical layer procedures, and radio resource management algorithms that help realize the potential advantages of D2D communications. In this article we use the 3GPP Long Term Evolution system as a baseline for D2D design, review some of the key design challenges, and propose solution approaches that allow cellular devices and D2D pairs to share spectrum resources and thereby increase the spectrum and energy efficiency of traditional cellular networks. Simulation results illustrate the viability of the proposed design.
Augmenting geospatial data provenance through metadata tracking in geospatial service chaining
(2010)

In a service-oriented environment, heterogeneous data from distributed data archiving centers and various geo-processing services are chained together dynamically to generate on-demand data products. Creating an executable service chain requires detailed specification of metadata for data sets and service instances. Using metadata tracking, semantics-enabled metadata are generated and propagated through a service chain. This metadata can be employed to validate a service chain, e.g. whether metadata preconditions on the input data of services can be satisfied. This paper explores how this metadata can be further exploited to augment geospatial data provenance, i.e., how a geospatial data product is derived. Provenance information is automatically captured during the metadata tracking process. Semantic Web technologies, including OWL and SPARQL, are used for representation and query of this provenance information. The approach can not only contribute to the automatic recording of geospatial data provenance, but also provide a more informed understanding of provenance information using Semantic Web technologies. & 2009 Elsevier Ltd. All rights reserved.
Accelerometer Based Joint Step Detection and Adaptive Step Length Estimation Algorithm Using Handheld Devices
(2015)

—The pedestrian inertial navigation systems are generally based on Pedestrian Dead Reckoning (PDR) algorithm. Considering the physiological characteristics of pedestrian movement, we use the cyclical characteristics and statistics of acceleration waveform and features which are associated with the walking speed to estimate the stride length. Due to the randomness of the pedestrian hand-held habit, the step events cannot always be detected by using the periods of zero velocity updates (ZUPTs). Furthermore, the signal patterns of the sensor could differ significantly depending on the carrying modes and the user’s hand motion. Hence, the step detection and the associated adaptive step length model using a handheld device equipped with accelerometer is required to obtain high-accurate measurements. To achieve this goal, a compositional algorithm of empirical formula and Back-propagation neural network by using handheld devices is proposed to estimate the step length, as well as achieve the accuracy of step detection higher than 98%. Furthermore, the proposed joint step detection and adaptive step length estimation algorithm can help much in the development of Pedestrian Navigation Devices (PNDs) based on the handheld inertial sensors.
Toward the Minimal Universal Petri Net
(2014)

A universal Petri net with 14 places, 42 transitions, and 218 arcs was built in the class of deterministic inhibitor Petri nets (DIPNs); it is based on the minimal Turing machine (TM) of Woods and Neary with 6 states, 4 symbols, and 23 instructions, directly simulated by a Petri net. Several techniques were developed, including bi-tag system (BTS) construction on a DIPN, special encoding of TM tape by two stacks, and concise subnets that implement arithmetic encoding operations. The simulation using the BTS has cubic time and linear space complexity, while the resulting universal net runs in exponential time and quadratic space with respect to the target net transitions' firing sequence length. The technique is applicable for simulating any TM by the Petri net.
Transference of kettlebell training to strength, power, and endurance.
(2013)

Kettlebells are a popular implement in many strength and conditioning programs, and their benefits are touted in popular literature, books, and videos. However, clinical data on their efficacy are limited. The purpose of this study was to examine whether kettlebell training transfers strength and power to weightlifting and powerlifting exercises and improves muscular endurance. Thirty-seven subjects were assigned to an experimental (EXP, n = 23; mean age = 40.9 ± 12.9 years) or a control group (CON; n = 14; mean age = 39.6 ± 15.8 years), range 18-72 years. The participants were required to perform assessments including a barbell clean and jerk, barbell bench press, maximal vertical jump, and 45° back extensions to volitional fatigue before and after a 10-week kettlebell training program. Training was structured in a group setting for 2 d·wk(-1) for 10 weeks. A repeated measures analysis of variance was conducted to determine group × time interactions and main effects. Post hoc pairwise comparisons were conducted when appropriate. Bench press revealed a time × group interaction and a main effect (p < 0.05). Clean and jerk and back extension demonstrated a trend toward a time × group interaction, but it did not reach significance (p = 0.053). However, clean and jerk did reveal a main effect for time (p < 0.05). No significant findings were reported for maximal vertical jump. The results demonstrate a transfer of power and strength in response to 10 weeks of training with kettlebells. Traditional training methods may not be convenient or accessible for strength and conditioning specialists, athletes, coaches, and recreational exercisers. The current data suggest that kettlebells may be an effective alternative tool to improve performance in weightlifting and powerlifting.
Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding
(2016)

Improved dense trajectory features have been successfully used in video-based action recognition problems, but their application to face processing is more challenging. In this paper, we propose a novel system that deals with the problem of emotion recognition in real-world videos, using improved dense trajectory, LGBP-TOP, and geometric features. In the proposed system, we detect the face and facial landmarks from each frame of a video using a combination of two recent approaches, and register faces by means of Procrustes analysis. The improved dense trajectory and geometric features are encoded using Fisher vectors and classification is achieved by extreme learning machines. We evaluate our method on the extended Cohn-Kanade (CK+) and EmotiW 2015 Challenge databases. We obtain state-of the-art results in both databases.
Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor
(2017)

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.
Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays
(2016)

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding.